With the breakthroughs in data science and machine learning, the most popular platforms such as Spotify, Amazon, Youtube, etc. are using recommendation systems to personalize their user’s experience, which offers significant value to both consumers and firms. For consumers, these systems help them reduce the effort of searching for appropriate products among large number of options. The efficiency of these systems is proved by the fact that 60% of choices on Netflix and 35% of sales on Amazon originate from recommendations (Lee et al., 2012). For firms, these systems promote their sales, and establish trust from users. Personalisation is stated to play an important role in success of top e-commerce company. Inspired by recommendation systems’ huge benefits, this qualified-self (QS) project harnesses the power of personal data to have insights into behaviors and preferences of people, which could be used to develop or improve these systems.
There are different factors determining what songs users listen or what products they purchase. For example, as human, we all go through ups and downs in life, which changes our moods on a daily basis. These moods can have a significant impact on our behavior such as music listening, or purchasing goods. Besides, people of the same ages or gender can share common tastes in music or habits of spending. Hence, our group collected and analysed three datasets including songs, expenses, and moods to explore:
The finding is used to examine above assumptions as well as check against findings from other research.
Apart from data collected by group, an individual data is also collected and analysed to find the relationship between users’ moods and features of songs that they listen to such as danceability, energy, etc.
Rationale:
Issues:
Rationale:
Issue:
The research also performed analysis on cohort data. There are two cohort data collected: average weekly expenditure of a lone under 35 in NSW (Australian Bureau of Statistics, 2015-16) and music peak preference by age (Stephens-Davidowitz, n.d). These are considered reliable data because they were published by Australian Bureau of Statistics and Spotify. While the former is used to compare against findings in Group expense data, the latter is used to compare against analysis of Group song data.
Data quality is assessed with reference to The Quarzt guide to bad data (Yanofsky, 2018). There are several issues identified in the datasets, which could be classified by their level of impact.
Resolved issues thereby having no impact on quality:
Minimal impact:
Serious impact:
Inestimable
For Group, Song and Expense datasets are analysed separately then each of them is combined with Moods dataset to find relationships. For Individual, Song dataset with Spotify variables is combined with Moods dataset to have deeper insights into relation between songs and moods as well as compare with analysis results from group data.
According to Davies et al. (2022), although music peak preferences vary, a large number of individuals prefer music released in their mid-to-late teens (15 - 19) most, and prefer music released earlier or later in their lives least. However, Figure 2.1 indicates a different trend - only 25% of songs that people listen to were released when they were in teens, 75% of songs were released after they were in twenties. Besides, in most cases, the release date range becomes larger when the age increases, which can be explained by the fact that older people listen to songs earlier. Moreover, some outliers indicate the that people still listen to songs released very early and even before they were born.
Figure 2.1: Age vs Release date.
Figure 2.2 reveals that Females have more narrow range of song duration (around 145 - 309 minutes) and outliers show that the maxiumn of song length is just 377 minutes. Meanwhile, males have broader range of duration (around 85 - 367 minutes) and they listen to songs with long duration (400 - 600 mins).
Figure 2.2: Distribution of song duration.
Based on Figure 2.3, younger members listen to more song genres compared with older members. However, a common trend can be seen from the figure - the most favorite song genres is either Pop or Indie, except for Felix who prefer Newage most. This can be explained by the fact that all members years of birth are from 1990 to 1999, an era of Pop and Indie.
Figure 2.3: Genre popularity by age.
Statistical analysis conducted by Xu et al. (2021) found that males prefer sad songs than female. Similarly, Figure 2.5 also illustrates that women listens to positive songs more than negative songs while it is opposite for men. However, Figure 2.4 reveals that both song sentiments of both males and females experienced fluctuation within a month.
Figure 2.4: Song sentiment score by gender.
Figure 2.5: Number of positive songs and negative songs.
Figure 2.6 demonstrates the expense of members over a month. It reveals that although members have varied amount of expenses, their expenses witnessed a certain seasonality, which is showed by significant increases in expense at specific points of time. Thyme had a tendency to spend more at every start of the week. Phoenix spent more on every Friday. Javier and Felix spent more in every middle of a week. Others member spent more every Friday-Monday.
Figure 2.6: Expense amount during a month.
In Figure 2.7, the amount spent on each time of purchasing was normally less $100 among all members. However, 25% of Phoenix’s purchases were more than $200. This can be explained by the fact that Phoenix’s first arrival to Australia was from Aug 11 while others had already lived in Australia for at least 1 month. Besides, outliers indicate that Felix, Phoenix and Thyme had several large purchases ($390 - $1100). It suggests that members having fulltime/parttime jobs are likely to spend larger amount of money.
Figure 2.7: Distribution of expense amount.
Figure 2.8 reveals the percentage of expenses for each type by gender. It can be seen that both males and females share a common spending behavior. The expense types from most spent to least spent: Groceries -> Utilities -> Transportation -> Medical.
Figure 2.8: Percentage of expense type by gender.
Figure 2.9 demonstrates the frequency of purchase for each expense type. It suggests that all members purchased Groceries most frequently. Besides, while four out of six members spent on Utilities second most frequently, others (Eren and Javier Pena) spent on it least frequently. This quite contradicts to conclusion from Figure 2.8, which may results from a higher price of Utilities compared to Groceries.
Figure 2.9: Frequency of purchase for each type.
Figure 2.10 illustrates changes in moods during a month of all members. First, there is no common pattern in mood changes among females or males. However, there is a common pattern in mood changes among Phoenix and Felix, which might be due to their moods being affected by dues (low moods a few days before assignment dues and high mood after assignment dues). Other members’ moods are more table.
Figure 2.10: Mood vs date
Figure 2.11 displays how females listened to song according to their moods. It can be concluded that females prefer positive songs over other types regardless of their moods, especially on Blissful life days. Furthermore, the lower their moods were, the more sad songs they listened to.
Figure 2.11: Percentage of each sentiment by mood type - Female.
On the contrary, Figure 2.12 suggested that males prefer negative songs over other types no matter how they felt. Overall, there is no considerable change in number of negative, neutral and sad songs they listened to when their moods changed. Moreover, it can be seen that males listened to more neutral songs than famales.
Figure 2.12: Percentage of each sentiment by mood type - Male.
It has been always assumed that sentiment of songs people listen to is affected by their emotions. However, Figure 2.13 demonstrates that song sentiments and a listener’s moods are not positively correlated. Besides, according to Mind (2022), people tend to overspend to feel better but Figure 2.13 indicates a low correlation between expense amount and moods.
Figure 2.13: Correlation between mood and song/expense.
Figure 2.14 and Figure 2.15 reveal that mood and song valence are significantly correlated. Because Spotify valence is known as song sentiment, this result was different from the finding in Figure 2.13 which shows a slight correlation - 0.3. There are two reasons for this situation: 1. either valence or sentiment analysis is inaccurate, 2. Songs collected per day do not represent the listener’s mood. Besides, there is only a moderate relation between mood and danceability/energy and no relation between mood and loudness. Therefore, it suggests that other song’s features such as danceability, energy, etc. relates to user moods. These variables are particular helpful for songs without lyrics.
Figure 2.14: Correlation between song variables and moods.
Figure 2.15: Moods vs positively correlated variables.
A research analysing Spotify data found that men are aged 13-16 when their favorite song is released, and for women, it’s age 11-14. The research concluded that regardless of gender, people are likely to stick to music they listened to the earliest phase of the adolescence. This finding, however, is different from what we found in our project.
Figure 2.16: Music preference peak vs age.
Figure 2.17 presents average weekly expenditure of a lone person under 35 in New South Wales. Unlike the finding in Figure 2.8, it is shown that people in NSW spent most on Utilities instead of Groceries. However, transportation and medical still keep their position of being the second least and least spent type.
Figure 2.17: Average weekly expenditure of a lone person under 35 in NSW.
The intention of this project is to find patterns in song listening, spending, and moods, as well as their relation. Therefore, various types of stakeholders that might be involved including e-commerce companies, music streaming companies, etc. In this section, different frameworks are applied to identify current issues as well as potential issues that can arise when project is conducted at larger scale.
Australian Privacy Principles (APP) is applied to evaluate Legalities and Privacy aspect of this project. The APP regulates how personal data should be handled, whether or not the data is accurate. Personal data also includes personal tastes, preferences, transaction history, music listening history, etc. Therefore, data usage in this project is subject to Privacy Act. However, the project does not fall under regulations of APP because it was conducted by a small group, not a business with over $3M revenue.
Imagining that the project is conducted at larger scale, compliance of the project with APP is checked in details in Appendix B. In summary, the project complies fully with 11/13 principles and partially complies with 2/13 principles. First, the project does not fully comply with APP 1 because the team did not take the APP into account throughout the project, thereby not establishing transparent practices, procedures, and systems to explicitly ensure our compliance with other APPs. However, the team already considered legal and privacy issues, therefore, apply several practices that align with other APPs. Second, APP 11 is not fully complied due to the risk of data loss and unauthorised modification: there is only one Google Sheet file collecting all data points and a member can intentionally/accidentally modify others’ records. Lastly, it should be noted that the project does not violate APP 7 because the project uses de-identified dataset that does not contain any email, phone numbers, or addresses. Besides, the stakeholders are expected to recommend their products via their users’ web page instead of direct communication via SMS, telephone, email, etc.
There are several strategies which could have been done to address the above misconduct. First, the team should have investigated into legalities then selected specific frameworks, law cases, etc. to apply throughout the project. For example, to comply with APP 1, the team needs to agree on using APP as a legal guidline, then establish clear process of maintaining APPs. Second, to prevent data loss, there should have been several backups, which can be stored in different secured platforms such as Drive, Dropbox, AWS, or Azure. This practice prevents losing data due to unexpected infrastructure destruction of any repository. Third, unauthorized modification could have been prevented by making backup read-only. Team members are only allowed to edit one dataset for data correction purpose, other backups are all read-only.
The project also uses data from third-party including Commonwealth Bank (transaction data) and Spotify (song data). There has always been a controversy over how applications deliver their terms and conditions. Citizen Advice reports that only 1% of people reading terms and conditions fully, which raised a significant concern about privacy (Ketchell, 2016). Wordy and unclear documents prevent users from being fully informed of their contract with providers. As the result, they do not acknowledge how their data is controlled or traded.
Not everything that is legal is ethical. Unlike legal principles, data ethics are established to evaluate potentially adverse impact of data practices on people society, thereby defining good practices for collecting, analysing, and sharing data. Open Data Institute’s Data Ethics Canvas (Appendix C) is the framework used for evaluating ethical aspects of this project.
Apart from ethical considerations in ODI, there are also several ethical issues around using users’ emotion for recommendation. In Jan 2021, Spotify was granted patent to use speech recognition to identify users’ feelings as a way to recommend songs. However, this innovation was strongly disapproved by nearly 200 musicians, bands, and civil rights groups due to concerns about monitoring people’s emotions, or even worse, manipulating them (Schwartz, 2021). With power of AI’s algorithm, detecting or altering human emotions via songs is no longer fictitious. Not only Spotify, other companies such as Amazon, Toyota, Cerene, etc. also have patent for similar functionality. Additionally, without a public and transparent explanation for their technology, we do not know what kind “voice” captured by this technology. Is it just a sound or a normal conversation ? If it is the latter, these companies are likely to violate privacy principles.
During the project, there are several difficulties that I encountered.
First, as mentioned above, due to lack of song lyrics, Felix’s songs are removed from sentiment analysis. However, this removal is not an optimal solution because the data is still valuable and findings could have been different if it had been included. This situation is also likely to happen in wider data science practice because music can be either instrumental or lyrical. Findings in Insights into song variables indicated a strong relation between mood and Spotify variables. Therefore, these variables can be replaced with lyrics in sentiment analysis, which avoids unnecessary data exclusion.
Second, the project should have been assessed using more Legal, Privacy and Ethical Frameworks, especially when it is conducted by organisation. For example, if stakeholder is a multinational company who collects data of citizens over the world, laws of different countries must be now considered.
Third, there is a concern about inaccurate findings due to lack of data. All data science projects need large enough sample to guarantee the reliability and greater precision of result. In this project, lack of data issue is caused by two main reasons: short period of time and manual log. In a larger project, data collecting should continue until it reaches certain data points. Besides, automated data collection is recommended due to its ease of collecting large data. For wider data science project, technologies such as Snowflake, Spark, Hadoop, etc. are commonly-used for data management and processing. Tools such as Excel or Drive is not preferable for data science projects.
Lee, & Hosanagar, K. (2019). How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment. Information Systems Research, 30(1), 239–259. https://doi.org/10.1287/isre.2018.0800
Stassen, M. (2021, January 27). Spotify’s Latest Invention Monitors Your Speech, Determines Your Emotional State… And Suggests Music Based On It. Music Business Worldwide. https://www.musicbusinessworldwide.com/spotifys-latest-invention-will-determine-your-emotional-state-from-your-speech-and-suggest-music-based-on-it/
Yanofsky, D. 2018, The Quartz guide to bad data, Github, viewed June 6, 2020, https://github.com/Quartz/bad-data-guide.
Moore, S. (2018). How to Create a Business Case for Data Quality Improvement. Gartner. www.gartner.com/smarterwithgartner/how-to-create-a-business-case-for-data-quality-improvement/.
IBM. (2018). Extracting Business Value From The 4 Vs of Big Data. https://www.ibmbigdatahub.com/infographic/extracting-business-value-4-vs-big-data.
Weitzel, L., Prati, R., & Aguiar, R. (2016). The Comprehension of Figurative Language: What Is the Influence of Irony and Sarcasm on NLP Techniques?. 10.1007/978-3-319-30319-2_3
Silge, J, & Robinson, D. (2022). Text Mining with R: A Tidy Approach. O’Reilly.
Mohammad, S. M. (2017). Challenges in Sentiment Analysis. A Practical Guide to Sentiment Analysis, (61-83). 10.1007/978-3-319-55394-8_4.
Roldós, I. (2020, December 23). Major Challenges of Natural Language Processing (NLP). Monkey Learn. https://monkeylearn.com/blog/natural-language-processing-challenges/
Ferjan, M. (2022, August 19). 30+ Official Listening to Music Statistics. Headphones Addict. https://headphonesaddict.com/listening-to-music-statistics/
Schwartz, D., Fischhoff, B., Krishnamurti, T., & Sowell, F. (2013). The Hawthorne Effect and Energy Awareness. Proc Natl Acad Sci U S A, 110(38), 15242–15246. 10.1073/pnas.1301687110
Davies, C., Page, B., Driesener, C. et al. (2022). The Power of Nostalgia: Age and Preference For Popular Music. Mark Lett. https://doi.org/10.1007/s11002-022-09626-7
Stephen-Davidowitz, S. (2018, February 10). Opinion: The Songs That Bind. The New York Times. https://www.nytimes.com/2018/02/10/opinion/sunday/favorite-songs.html
Stern, M. J. (2014, August 12). Neural Nostalgia: Why Do We Love The Music We Heard As Teenagers?. Slate. https://slate.com/technology/2014/08/musical-nostalgia-the-psychology-and-neuroscience-for-song-preference-and-the-reminiscence-bump.html#:~:text=And%20researchers%20have%20uncovered%20evidence,t%20weaken%20as%20we%20age.
Ong, T. (2018, February 2). Our Musical Tastes Peak As Teens, Says Study. The Verge. https://www.theverge.com/2018/2/12/17003076/spotify-data-shows-songs-teens-adult-taste-music
Stephens-Davidowitz, S. (n.d.). Research. Sethsd. http://sethsd.com/research
Xu, L., Zheng, Y., Xu, D., & Xu, L. (2021). Predicting the Preference for Sad Music: The Role of Gender, Personality, and Audio Features. IEEE Access. 1-1. 10.1109/ACCESS.2021.3090940.
Library. (n.d.). 6.2. The Evolution of Popular Music. https://open.lib.umn.edu/mediaandculture/chapter/6-2-the-evolution-of-popular-music/
Mind. (2022 April). The Link Between Money and Mental Health. https://www.mind.org.uk/information-support/tips-for-everyday-living/money-and-mental-health/the-link-between-money-and-mental-health/#mental-health-can-affect-money
AIAAIC. (n.d.). About the AIA Repository. https://www.aiaaic.org/aiaaic-repository/about-the-aiaaic-repository
Ketchell, M. (2016, July 15). Never Read The Terms and Conditions? Here’s An Idea That Might Protect Your Online Privacy. The Conversation. https://theconversation.com/never-read-the-terms-and-conditions-heres-an-idea-that-might-protect-your-online-privacy-62208
Thornhill, J. (2016, January 20). Brave New Era In Technology Needs New Ethics. Financial Times. https://www.ft.com/content/dd328bf4-a25e-11e5-8d70-42b68cfae6e4
Office of The Australian Information Commissioner. (n.d.). Australian Privacy Principles Quick Reference. https://www.oaic.gov.au/privacy/australian-privacy-principles/australian-privacy-principles-quick-reference
Schwartz, E. H. (2021, May 7). Musicians Demand Spotify Not Develop Emotional Speech Recognition Patent. Voicebot. https://voicebot.ai/2021/05/07/musicians-demand-spotify-not-develop-emotional-speech-recognition-patent/
Davie, O. (2021, October 2). Spotify Patents Tech To Monitor Your Speech, Infer Emotion. HypeBot. https://www.hypebot.com/hypebot/2021/02/spotify-patents-tech-to-monitor-your-speech-infer-emotion.html
Australian Bureau of Statistics. (2015-16). Household Expenditure Survey, Australia: Summary of Results. ABS. https://www.abs.gov.au/statistics/economy/finance/household-expenditure-survey-australia-summary-results/latest-release.
Stephens-Davidowitz, S. (n.d.). Research. Sethsd. http://sethsd.com/research
Songs - Group
## # A tibble: 662 × 14
## date title artist genre durat…¹ release_…² lyrics ident…³ gender age
## <date> <chr> <chr> <chr> <chr> <date> <chr> <chr> <chr> <dbl>
## 1 2022-08-17 Heart … LANY Alte… 03:19:… 2020-10-02 heart… Javier… Male 30
## 2 2022-08-17 A Sky … Coldp… Alte… 04:29:… 2014-05-02 a sky… Javier… Male 30
## 3 2022-08-17 Your n… Demxn… Soul 02:35:… 2018-04-07 danci… Javier… Male 30
## 4 2022-08-18 She kn… Demxn… Soul 03:40:… 2017-09-30 take … Javier… Male 30
## 5 2022-08-18 ILYSB LANY Alte… 04:05:… 2015-12-11 ilysb… Javier… Male 30
## 6 2022-08-18 Thick … LANY Alte… 03:32:… 2018-09-11 thick… Javier… Male 30
## 7 2022-08-19 Heart … LANY Alte… 03:19:… 2020-10-02 heart… Javier… Male 30
## 8 2022-08-19 Learn … Foo F… Alte… 03:56:… 1999-09-18 learn… Javier… Male 30
## # … with 654 more rows, 4 more variables: duration_min <dbl>,
## # formatted_lyrics <chr>, score <dbl>, sentiment <chr>, and abbreviated
## # variable names ¹duration, ²release_date, ³identifier
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
Expenses - Group
## # A tibble: 538 × 6
## date amount description type identifier gender
## <date> <dbl> <chr> <chr> <chr> <chr>
## 1 2022-08-15 15 <NA> Utilities Arnald Female
## 2 2022-08-16 10 <NA> Utilities Arnald Female
## 3 2022-08-17 0 <NA> No spend Arnald Female
## 4 2022-08-18 0 <NA> No spend Arnald Female
## 5 2022-08-19 20 <NA> Transportation Arnald Female
## 6 2022-08-19 32.0 <NA> Groceries Arnald Female
## 7 2022-08-20 0 <NA> No spend Arnald Female
## 8 2022-08-21 0 <NA> No spend Arnald Female
## # … with 530 more rows
## # ℹ Use `print(n = ...)` to see more rows
Moods - Group
## # A tibble: 253 × 5
## date mood identifier gender mood_type
## <date> <dbl> <chr> <chr> <chr>
## 1 2022-08-11 6 Javier Pena Male Surviving
## 2 2022-08-12 6 Javier Pena Male Surviving
## 3 2022-08-13 7 Javier Pena Male Blissful life
## 4 2022-08-14 8 Javier Pena Male Blissful life
## 5 2022-08-15 6 Javier Pena Male Surviving
## 6 2022-08-16 7 Javier Pena Male Blissful life
## 7 2022-08-17 6 Javier Pena Male Surviving
## 8 2022-08-18 7 Javier Pena Male Blissful life
## # … with 245 more rows
## # ℹ Use `print(n = ...)` to see more rows
Song variables - Individual
## # A tibble: 215 × 6
## date name dance…¹ energy loudn…² valence
## <date> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2022-08-11 When I Was Your Man 0.612 0.28 -8.65 0.387
## 2 2022-08-11 Higher Love 0.693 0.678 -7.16 0.404
## 3 2022-08-11 Dive 0.654 0.787 -5.00 0.409
## 4 2022-08-11 Mariposa 0.676 0.525 -5.88 0.421
## 5 2022-08-11 Don’t Wake Me Up 0.621 0.747 -5.08 0.426
## 6 2022-08-12 My Head & My Heart 0.614 0.934 -3.71 0.436
## 7 2022-08-12 Wellerman - Sea Shanty / 220 KID x … 0.722 0.893 -3.26 0.439
## 8 2022-08-12 Pepas 0.762 0.766 -3.96 0.442
## # … with 207 more rows, and abbreviated variable names ¹danceability, ²loudness
## # ℹ Use `print(n = ...)` to see more rows
| Principle | Title | Purpose | Compliance |
|---|---|---|---|
| APP 1 | Open and transparent management of personal information |
Ensures that APP entities manage personal information in an open and transparent way. This includes having a clearly expressed and up to date APP privacy policy. |
Partially |
| APP 2 | Anonymity and pseudonymity | Requires APP entities to give individuals the option of not identifying themselves, or of using a pseudonym. Limited exceptions apply. |
Yes |
| APP 3 | Collection of solicited personal information |
Outlines when an APP entity can collect personal information that is solicited. It applies higher standards to the collection of ‘sensitive’ information. |
Yes |
| APP 4 | Dealing with unsolicited personal information |
Outlines how APP entities must deal with unsolicited personal information. | Yes |
| APP 5 | Notification of the collection of personal information |
Outlines when and in what circumstances an APP entity that collects personal information must notify an individual of certain matters. |
Yes |
| APP 6 | Use or disclosure of personal information |
Outlines the circumstances in which an APP entity may use or disclose personal information that it holds. |
Yes |
| APP 7 | Direct marketing | An organisation may only use or disclose personal information for direct marketing purposes if certain conditions are met. |
Yes |
| APP 8 | Cross-border disclosure of personal information |
Outlines the steps an APP entity must take to protect personal information before it is disclosed overseas. |
Yes |
| APP 9 | Adoption, use or disclosure of government related identifiers |
Outlines the limited circumstances when an organisation may adopt a government related identifier of an individual as its own identifier, or use or disclose a government related identifier of an individual. |
Yes |
| APP 10 | Quality of personal information | An APP entity must take reasonable steps to ensure the personal information it collects is accurate, up to date and complete. An entity must also take reasonable steps to ensure the personal information it uses or discloses is accurate, up to date, complete and relevant, having regard to the purpose of the use or disclosure. |
Yes |
| APP 11 | Security of personal information | An APP entity must take reasonable steps to protect personal information it holds from misuse, interference and loss, and from unauthorised access, modification or disclosure. An entity has obligations to destroy or de-identify personal information in certain circumstances. |
Partially |
| APP 12 | Access to personal information | Outlines an APP entity’s obligations when an individual requests to be given access to personal information held about them by the entity. This includes a requirement to provide access unless a specific exception applies. |
Yes |
| APP 13 | Correction of personal information | Outlines an APP entity’s obligations in relation to correcting the personal information it holds about individuals. |
Yes |
| type |
|---|
| Utilities |
| Groceries |
| Transportation |
| Transport |
| Medical |
## # A tibble: 2 × 10
## date title artist genre durat…¹ relea…² lyrics ident…³ gender age
## <date> <chr> <chr> <chr> <time> <chr> <chr> <chr> <chr> <dbl>
## 1 0202-08-17 Reconfigu… Other… Indie 03:27 04/05/… i won… Thyme Male 27
## 2 0202-08-17 Basket Ca… Green… Punk… 03:01 29/08/… baske… Eren Male 24
## # … with abbreviated variable names ¹duration, ²release_date, ³identifier
| date | title | artist | genre | duration | release_date | identifier | gender | age |
|---|---|---|---|---|---|---|---|---|
| 2022-09-04 | The Ringer | Eminem | HipHop | 05:37:00 | 2108-08-31 | Eren | Male | 24 |